Overview

Brought to you by YData

Dataset statistics

Number of variables17
Number of observations294419
Missing cells0
Missing cells (%)0.0%
Duplicate rows7261
Duplicate rows (%)2.5%
Total size in memory38.2 MiB
Average record size in memory136.0 B

Variable types

Numeric13
Categorical4

Alerts

Dataset has 7261 (2.5%) duplicate rowsDuplicates
adv_s is highly overall correlated with join_r and 2 other fieldsHigh correlation
data_r is highly overall correlated with data_sent_to_bs and 5 other fieldsHigh correlation
data_s is highly overall correlated with join_s and 2 other fieldsHigh correlation
data_sent_to_bs is highly overall correlated with data_r and 2 other fieldsHigh correlation
dist_ch_to_bs is highly overall correlated with data_r and 3 other fieldsHigh correlation
is_target is highly overall correlated with join_s and 1 other fieldsHigh correlation
join_r is highly overall correlated with adv_s and 2 other fieldsHigh correlation
join_s is highly overall correlated with data_r and 8 other fieldsHigh correlation
rank is highly overall correlated with adv_sHigh correlation
sch_r is highly overall correlated with data_r and 5 other fieldsHigh correlation
sch_s is highly overall correlated with data_r and 3 other fieldsHigh correlation
send_code is highly overall correlated with adv_s and 2 other fieldsHigh correlation
time is highly overall correlated with join_s and 1 other fieldsHigh correlation
who_ch is highly overall correlated with join_s and 2 other fieldsHigh correlation
sch_s is highly imbalanced (74.8%) Imbalance
is_target is highly imbalanced (75.4%) Imbalance
expaned_energy is highly skewed (γ1 = 27.03886779) Skewed
dist_to_ch has 41847 (14.2%) zeros Zeros
adv_s has 265019 (90.0%) zeros Zeros
adv_r has 7557 (2.6%) zeros Zeros
join_r has 281976 (95.8%) zeros Zeros
rank has 35459 (12.0%) zeros Zeros
data_s has 45053 (15.3%) zeros Zeros
data_r has 267201 (90.8%) zeros Zeros
data_sent_to_bs has 260289 (88.4%) zeros Zeros
dist_ch_to_bs has 260289 (88.4%) zeros Zeros
send_code has 41806 (14.2%) zeros Zeros

Reproduction

Analysis started2024-10-22 01:03:35.363028
Analysis finished2024-10-22 01:03:58.768305
Duration23.41 seconds
Software versionydata-profiling vv4.11.0
Download configurationconfig.json

Variables

time
Real number (ℝ)

High correlation 

Distinct144
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean713.42014
Minimum50
Maximum3203
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2024-10-21T22:03:58.858853image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile103
Q1303
median603
Q3953
95-th percentile1553
Maximum3203
Range3153
Interquartile range (IQR)650

Descriptive statistics

Standard deviation559.88438
Coefficient of variation (CV)0.78478915
Kurtosis3.9041704
Mean713.42014
Median Absolute Deviation (MAD)350
Skewness1.6645677
Sum2.1004444 × 108
Variance313470.52
MonotonicityIncreasing
2024-10-21T22:03:58.990735image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53 13435
 
4.6%
103 13360
 
4.5%
253 13327
 
4.5%
153 13029
 
4.4%
203 12945
 
4.4%
303 12882
 
4.4%
353 12542
 
4.3%
403 11765
 
4.0%
453 11454
 
3.9%
503 11363
 
3.9%
Other values (134) 168317
57.2%
ValueCountFrequency (%)
50 300
 
0.1%
53 13435
4.6%
100 300
 
0.1%
103 13360
4.5%
150 300
 
0.1%
153 13029
4.4%
200 300
 
0.1%
203 12945
4.4%
250 300
 
0.1%
253 13327
4.5%
ValueCountFrequency (%)
3203 162
 
0.1%
3153 585
0.2%
3103 521
0.2%
3053 519
0.2%
3003 556
0.2%
2953 511
0.2%
2903 485
0.2%
2853 532
0.2%
2803 490
0.2%
2753 498
0.2%

who_ch
Real number (ℝ)

High correlation 

Distinct5686
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean167967.68
Minimum101000
Maximum2901100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2024-10-21T22:03:59.121830image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum101000
5-th percentile102005
Q1106046
median112081
Q3201065
95-th percentile407100
Maximum2901100
Range2800100
Interquartile range (IQR)95019

Descriptive statistics

Standard deviation174421.21
Coefficient of variation (CV)1.0384212
Kurtosis60.470734
Mean167967.68
Median Absolute Deviation (MAD)7004
Skewness6.5075764
Sum4.9452878 × 1010
Variance3.0422757 × 1010
MonotonicityNot monotonic
2024-10-21T22:03:59.255245image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
202100 3528
 
1.2%
201100 1991
 
0.7%
203100 1593
 
0.5%
301100 927
 
0.3%
205100 905
 
0.3%
204100 867
 
0.3%
111100 846
 
0.3%
206100 807
 
0.3%
110100 777
 
0.3%
108100 746
 
0.3%
Other values (5676) 281432
95.6%
ValueCountFrequency (%)
101000 222
0.1%
101001 389
0.1%
101002 111
 
< 0.1%
101003 281
0.1%
101005 286
0.1%
101006 252
0.1%
101007 133
 
< 0.1%
101008 76
 
< 0.1%
101009 245
0.1%
101010 93
 
< 0.1%
ValueCountFrequency (%)
2901100 1
 
< 0.1%
2901001 1
 
< 0.1%
2802100 6
< 0.1%
2802096 1
 
< 0.1%
2802054 1
 
< 0.1%
2802034 1
 
< 0.1%
2802001 1
 
< 0.1%
2801100 6
< 0.1%
2801096 1
 
< 0.1%
2801054 1
 
< 0.1%

dist_to_ch
Real number (ℝ)

Zeros 

Distinct13832
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.583892
Minimum0
Maximum214.27462
Zeros41847
Zeros (%)14.2%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2024-10-21T22:03:59.389249image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q19.91917
median21.46811
Q336.52223
95-th percentile65.43427
Maximum214.27462
Range214.27462
Interquartile range (IQR)26.60306

Descriptive statistics

Standard deviation22.299264
Coefficient of variation (CV)0.87161342
Kurtosis5.7552263
Mean25.583892
Median Absolute Deviation (MAD)12.81299
Skewness1.6812511
Sum7532383.9
Variance497.25716
MonotonicityNot monotonic
2024-10-21T22:03:59.770091image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 41847
 
14.2%
5.96867 239
 
0.1%
8.97549 222
 
0.1%
8.75269 215
 
0.1%
12.26259 193
 
0.1%
10.67763 182
 
0.1%
4.73337 177
 
0.1%
1.23076 176
 
0.1%
3.0131 175
 
0.1%
9.0031 173
 
0.1%
Other values (13822) 250820
85.2%
ValueCountFrequency (%)
0 41847
14.2%
0.40092 84
 
< 0.1%
0.48993 75
 
< 0.1%
0.66713 81
 
< 0.1%
0.67335 82
 
< 0.1%
0.80238 42
 
< 0.1%
0.83439 70
 
< 0.1%
0.88798 135
 
< 0.1%
1.1225 146
 
< 0.1%
1.23076 176
 
0.1%
ValueCountFrequency (%)
214.27462 25
< 0.1%
213.85247 21
< 0.1%
206.32935 9
 
< 0.1%
205.22083 9
 
< 0.1%
203.79135 8
 
< 0.1%
202.98739 7
 
< 0.1%
190.43411 5
 
< 0.1%
186.94848 7
 
< 0.1%
184.31794 6
 
< 0.1%
183.55017 7
 
< 0.1%

adv_s
Real number (ℝ)

High correlation  Zeros 

Distinct85
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.26374996
Minimum0
Maximum97
Zeros265019
Zeros (%)90.0%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2024-10-21T22:03:59.900374image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum97
Range97
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.189291
Coefficient of variation (CV)8.3006306
Kurtosis527.42395
Mean0.26374996
Median Absolute Deviation (MAD)0
Skewness19.365275
Sum77653
Variance4.7929951
MonotonicityNot monotonic
2024-10-21T22:04:00.036468image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 265019
90.0%
1 26403
 
9.0%
11 162
 
0.1%
18 161
 
0.1%
17 155
 
0.1%
10 155
 
0.1%
14 151
 
0.1%
19 150
 
0.1%
16 149
 
0.1%
13 147
 
< 0.1%
Other values (75) 1767
 
0.6%
ValueCountFrequency (%)
0 265019
90.0%
1 26403
 
9.0%
3 113
 
< 0.1%
4 104
 
< 0.1%
5 114
 
< 0.1%
6 123
 
< 0.1%
7 89
 
< 0.1%
8 105
 
< 0.1%
9 92
 
< 0.1%
10 155
 
0.1%
ValueCountFrequency (%)
97 2
 
< 0.1%
96 3
< 0.1%
93 2
 
< 0.1%
92 2
 
< 0.1%
91 2
 
< 0.1%
90 1
 
< 0.1%
88 2
 
< 0.1%
87 1
 
< 0.1%
84 1
 
< 0.1%
83 5
< 0.1%

adv_r
Real number (ℝ)

Zeros 

Distinct31
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.2871961
Minimum0
Maximum117
Zeros7557
Zeros (%)2.6%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2024-10-21T22:04:00.154855image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14
median5
Q37
95-th percentile27
Maximum117
Range117
Interquartile range (IQR)3

Descriptive statistics

Standard deviation7.0357426
Coefficient of variation (CV)0.96549379
Kurtosis9.7784835
Mean7.2871961
Median Absolute Deviation (MAD)2
Skewness2.4372324
Sum2145489
Variance49.501674
MonotonicityNot monotonic
2024-10-21T22:04:00.274336image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
4 47116
16.0%
5 42988
14.6%
6 36253
12.3%
3 33465
11.4%
7 29212
9.9%
2 21009
7.1%
8 16954
 
5.8%
9 10907
 
3.7%
28 10394
 
3.5%
0 7557
 
2.6%
Other values (21) 38564
13.1%
ValueCountFrequency (%)
0 7557
 
2.6%
1 7499
 
2.5%
2 21009
7.1%
3 33465
11.4%
4 47116
16.0%
5 42988
14.6%
6 36253
12.3%
7 29212
9.9%
8 16954
 
5.8%
9 10907
 
3.7%
ValueCountFrequency (%)
117 34
 
< 0.1%
29 7
 
< 0.1%
28 10394
3.5%
27 7274
2.5%
26 5312
1.8%
25 2966
 
1.0%
24 1594
 
0.5%
23 855
 
0.3%
22 652
 
0.2%
21 718
 
0.2%

join_s
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
1
252613 
0
41806 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters294419
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 252613
85.8%
0 41806
 
14.2%

Length

2024-10-21T22:04:00.390376image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-21T22:04:00.486379image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 252613
85.8%
0 41806
 
14.2%

Most occurring characters

ValueCountFrequency (%)
1 252613
85.8%
0 41806
 
14.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 294419
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 252613
85.8%
0 41806
 
14.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 294419
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 252613
85.8%
0 41806
 
14.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 294419
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 252613
85.8%
0 41806
 
14.2%

join_r
Real number (ℝ)

High correlation  Zeros 

Distinct101
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.60952588
Minimum0
Maximum124
Zeros281976
Zeros (%)95.8%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2024-10-21T22:04:00.592323image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum124
Range124
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.3064044
Coefficient of variation (CV)7.0651708
Kurtosis158.88879
Mean0.60952588
Median Absolute Deviation (MAD)0
Skewness11.029409
Sum179456
Variance18.545119
MonotonicityNot monotonic
2024-10-21T22:04:00.729317image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 281976
95.8%
1 1238
 
0.4%
2 892
 
0.3%
3 818
 
0.3%
4 680
 
0.2%
5 639
 
0.2%
6 566
 
0.2%
7 494
 
0.2%
8 454
 
0.2%
9 428
 
0.1%
Other values (91) 6234
 
2.1%
ValueCountFrequency (%)
0 281976
95.8%
1 1238
 
0.4%
2 892
 
0.3%
3 818
 
0.3%
4 680
 
0.2%
5 639
 
0.2%
6 566
 
0.2%
7 494
 
0.2%
8 454
 
0.2%
9 428
 
0.1%
ValueCountFrequency (%)
124 1
 
< 0.1%
99 38
< 0.1%
98 7
 
< 0.1%
97 4
 
< 0.1%
96 1
 
< 0.1%
95 3
 
< 0.1%
94 3
 
< 0.1%
93 2
 
< 0.1%
92 3
 
< 0.1%
91 2
 
< 0.1%

sch_s
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
0
281988 
1
 
12431

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters294419
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 281988
95.8%
1 12431
 
4.2%

Length

2024-10-21T22:04:00.850236image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-21T22:04:00.935541image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 281988
95.8%
1 12431
 
4.2%

Most occurring characters

ValueCountFrequency (%)
0 281988
95.8%
1 12431
 
4.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 294419
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 281988
95.8%
1 12431
 
4.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 294419
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 281988
95.8%
1 12431
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 294419
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 281988
95.8%
1 12431
 
4.2%

sch_r
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
1
241283 
0
53136 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters294419
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 241283
82.0%
0 53136
 
18.0%

Length

2024-10-21T22:04:01.030837image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-21T22:04:01.117516image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 241283
82.0%
0 53136
 
18.0%

Most occurring characters

ValueCountFrequency (%)
1 241283
82.0%
0 53136
 
18.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 294419
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 241283
82.0%
0 53136
 
18.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 294419
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 241283
82.0%
0 53136
 
18.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 294419
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 241283
82.0%
0 53136
 
18.0%

rank
Real number (ℝ)

High correlation  Zeros 

Distinct100
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.557437
Minimum0
Maximum99
Zeros35459
Zeros (%)12.0%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2024-10-21T22:04:01.224153image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median5
Q316
95-th percentile45
Maximum99
Range99
Interquartile range (IQR)15

Descriptive statistics

Standard deviation15.653245
Coefficient of variation (CV)1.3543872
Kurtosis5.270997
Mean11.557437
Median Absolute Deviation (MAD)5
Skewness2.1448203
Sum3402729
Variance245.02407
MonotonicityNot monotonic
2024-10-21T22:04:01.358374image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 73805
25.1%
0 35459
 
12.0%
3 11465
 
3.9%
2 11052
 
3.8%
5 9850
 
3.3%
4 9330
 
3.2%
7 8651
 
2.9%
6 8136
 
2.8%
9 7749
 
2.6%
8 7088
 
2.4%
Other values (90) 111834
38.0%
ValueCountFrequency (%)
0 35459
12.0%
1 73805
25.1%
2 11052
 
3.8%
3 11465
 
3.9%
4 9330
 
3.2%
5 9850
 
3.3%
6 8136
 
2.8%
7 8651
 
2.9%
8 7088
 
2.4%
9 7749
 
2.6%
ValueCountFrequency (%)
99 40
< 0.1%
98 51
< 0.1%
97 63
< 0.1%
96 52
< 0.1%
95 65
< 0.1%
94 65
< 0.1%
93 80
< 0.1%
92 75
< 0.1%
91 82
< 0.1%
90 82
< 0.1%

data_s
Real number (ℝ)

High correlation  Zeros 

Distinct186
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.237614
Minimum0
Maximum241
Zeros45053
Zeros (%)15.3%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2024-10-21T22:04:01.487990image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q118
median37
Q360
95-th percentile120
Maximum241
Range241
Interquartile range (IQR)42

Descriptive statistics

Standard deviation38.457455
Coefficient of variation (CV)0.86933836
Kurtosis3.2940095
Mean44.237614
Median Absolute Deviation (MAD)21
Skewness1.4963937
Sum13024394
Variance1478.9758
MonotonicityNot monotonic
2024-10-21T22:04:01.621922image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 45053
 
15.3%
13 13854
 
4.7%
55 6237
 
2.1%
76 5918
 
2.0%
62 5838
 
2.0%
85 5761
 
2.0%
51 5709
 
1.9%
53 5702
 
1.9%
24 5685
 
1.9%
65 5669
 
1.9%
Other values (176) 188993
64.2%
ValueCountFrequency (%)
0 45053
15.3%
1 9
 
< 0.1%
2 191
 
0.1%
3 249
 
0.1%
4 335
 
0.1%
5 438
 
0.1%
6 511
 
0.2%
7 428
 
0.1%
8 373
 
0.1%
9 307
 
0.1%
ValueCountFrequency (%)
241 714
0.2%
240 1
 
< 0.1%
237 1
 
< 0.1%
235 1
 
< 0.1%
229 1
 
< 0.1%
226 1
 
< 0.1%
220 1
 
< 0.1%
206 1195
0.4%
204 1
 
< 0.1%
202 2
 
< 0.1%

data_r
Real number (ℝ)

High correlation  Zeros 

Distinct1294
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66.187332
Minimum0
Maximum1496
Zeros267201
Zeros (%)90.8%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2024-10-21T22:04:01.747490image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile664.1
Maximum1496
Range1496
Interquartile range (IQR)0

Descriptive statistics

Standard deviation238.58994
Coefficient of variation (CV)3.6047674
Kurtosis13.469989
Mean66.187332
Median Absolute Deviation (MAD)0
Skewness3.7870401
Sum19486808
Variance56925.157
MonotonicityNot monotonic
2024-10-21T22:04:01.882060image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 267201
90.8%
241 1153
 
0.4%
351 825
 
0.3%
412 710
 
0.2%
117 673
 
0.2%
611 625
 
0.2%
543 569
 
0.2%
91 500
 
0.2%
640 464
 
0.2%
720 445
 
0.2%
Other values (1284) 21254
 
7.2%
ValueCountFrequency (%)
0 267201
90.8%
1 142
 
< 0.1%
2 1
 
< 0.1%
4 2
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
12 4
 
< 0.1%
13 1
 
< 0.1%
14 3
 
< 0.1%
ValueCountFrequency (%)
1496 1
 
< 0.1%
1494 4
 
< 0.1%
1493 2
 
< 0.1%
1492 10
< 0.1%
1491 6
< 0.1%
1490 1
 
< 0.1%
1489 3
 
< 0.1%
1488 2
 
< 0.1%
1487 5
< 0.1%
1486 5
< 0.1%

data_sent_to_bs
Real number (ℝ)

High correlation  Zeros 

Distinct223
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8708949
Minimum0
Maximum241
Zeros260289
Zeros (%)88.4%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2024-10-21T22:04:02.017230image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile13
Maximum241
Range241
Interquartile range (IQR)0

Descriptive statistics

Standard deviation19.298891
Coefficient of variation (CV)4.9856407
Kurtosis67.066136
Mean3.8708949
Median Absolute Deviation (MAD)0
Skewness7.6004568
Sum1139665
Variance372.4472
MonotonicityNot monotonic
2024-10-21T22:04:02.152729image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 260289
88.4%
13 12979
 
4.4%
1 2347
 
0.8%
2 1368
 
0.5%
3 940
 
0.3%
6 706
 
0.2%
4 670
 
0.2%
8 633
 
0.2%
5 625
 
0.2%
7 579
 
0.2%
Other values (213) 13283
 
4.5%
ValueCountFrequency (%)
0 260289
88.4%
1 2347
 
0.8%
2 1368
 
0.5%
3 940
 
0.3%
4 670
 
0.2%
5 625
 
0.2%
6 706
 
0.2%
7 579
 
0.2%
8 633
 
0.2%
9 545
 
0.2%
ValueCountFrequency (%)
241 245
0.1%
240 36
 
< 0.1%
239 21
 
< 0.1%
238 19
 
< 0.1%
237 7
 
< 0.1%
236 9
 
< 0.1%
235 3
 
< 0.1%
234 4
 
< 0.1%
233 3
 
< 0.1%
232 6
 
< 0.1%

dist_ch_to_bs
Real number (ℝ)

High correlation  Zeros 

Distinct305
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.764395
Minimum0
Maximum201.93494
Zeros260289
Zeros (%)88.4%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2024-10-21T22:04:02.283259image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile135.58222
Maximum201.93494
Range201.93494
Interquartile range (IQR)0

Descriptive statistics

Standard deviation42.050197
Coefficient of variation (CV)2.8480812
Kurtosis5.609535
Mean14.764395
Median Absolute Deviation (MAD)0
Skewness2.6672493
Sum4346918.5
Variance1768.219
MonotonicityNot monotonic
2024-10-21T22:04:02.424065image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 260289
88.4%
136.03878 627
 
0.2%
159.31297 609
 
0.2%
88.47785 507
 
0.2%
93.93772 500
 
0.2%
165.46205 471
 
0.2%
123.96292 451
 
0.2%
102.66424 415
 
0.1%
201.93494 408
 
0.1%
181.31284 405
 
0.1%
Other values (295) 29737
 
10.1%
ValueCountFrequency (%)
0 260289
88.4%
54.93262 402
 
0.1%
75.52972 102
 
< 0.1%
76.04676 91
 
< 0.1%
77.63787 70
 
< 0.1%
77.82286 57
 
< 0.1%
78.70375 63
 
< 0.1%
78.91449 123
 
< 0.1%
79.19069 123
 
< 0.1%
79.55137 74
 
< 0.1%
ValueCountFrequency (%)
201.93494 408
0.1%
181.31284 405
0.1%
176.98899 70
 
< 0.1%
176.62353 93
 
< 0.1%
176.42103 57
 
< 0.1%
176.40744 215
0.1%
176.34359 58
 
< 0.1%
175.01596 61
 
< 0.1%
174.29523 63
 
< 0.1%
173.64043 94
 
< 0.1%

send_code
Real number (ℝ)

High correlation  Zeros 

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7051821
Minimum0
Maximum15
Zeros41806
Zeros (%)14.2%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2024-10-21T22:04:02.540087image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q34
95-th percentile7
Maximum15
Range15
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.353416
Coefficient of variation (CV)0.86996585
Kurtosis3.5150116
Mean2.7051821
Median Absolute Deviation (MAD)1
Skewness1.4968583
Sum796457
Variance5.5385669
MonotonicityNot monotonic
2024-10-21T22:04:02.646569image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
1 66518
22.6%
2 55128
18.7%
3 43430
14.8%
0 41806
14.2%
4 32907
11.2%
5 21788
 
7.4%
6 13299
 
4.5%
7 8616
 
2.9%
8 4213
 
1.4%
9 1964
 
0.7%
Other values (6) 4750
 
1.6%
ValueCountFrequency (%)
0 41806
14.2%
1 66518
22.6%
2 55128
18.7%
3 43430
14.8%
4 32907
11.2%
5 21788
 
7.4%
6 13299
 
4.5%
7 8616
 
2.9%
8 4213
 
1.4%
9 1964
 
0.7%
ValueCountFrequency (%)
15 605
 
0.2%
14 553
 
0.2%
13 786
 
0.3%
12 684
 
0.2%
11 876
 
0.3%
10 1246
 
0.4%
9 1964
 
0.7%
8 4213
 
1.4%
7 8616
2.9%
6 13299
4.5%

expaned_energy
Real number (ℝ)

Skewed 

Distinct57467
Distinct (%)19.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.28442205
Minimum0
Maximum45.09394
Zeros10
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size2.2 MiB
2024-10-21T22:04:02.772743image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.019169
Q10.05306
median0.08745
Q30.1598
95-th percentile1.716162
Maximum45.09394
Range45.09394
Interquartile range (IQR)0.10674

Descriptive statistics

Standard deviation0.70894659
Coefficient of variation (CV)2.4925866
Kurtosis1621.3315
Mean0.28442205
Median Absolute Deviation (MAD)0.04201
Skewness27.038868
Sum83739.257
Variance0.50260527
MonotonicityNot monotonic
2024-10-21T22:04:02.900964image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.00447 876
 
0.3%
0.00448 627
 
0.2%
0.00446 506
 
0.2%
0.00606 391
 
0.1%
0.00607 388
 
0.1%
0.00608 274
 
0.1%
0.0043 272
 
0.1%
0.00449 252
 
0.1%
0.00583 232
 
0.1%
0.0061 213
 
0.1%
Other values (57457) 290388
98.6%
ValueCountFrequency (%)
0 10
 
< 0.1%
0.00093 8
 
< 0.1%
0.00108 62
< 0.1%
0.00167 1
 
< 0.1%
0.00168 15
 
< 0.1%
0.00169 27
< 0.1%
0.0017 11
 
< 0.1%
0.00171 13
 
< 0.1%
0.00172 22
 
< 0.1%
0.00173 22
 
< 0.1%
ValueCountFrequency (%)
45.09394 1
< 0.1%
45.09063 1
< 0.1%
45.07812 1
< 0.1%
45.07668 1
< 0.1%
45.07618 1
< 0.1%
45.07489 1
< 0.1%
45.0745 1
< 0.1%
45.07431 1
< 0.1%
45.07407 1
< 0.1%
45.07405 1
< 0.1%

is_target
Categorical

High correlation  Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.2 MiB
0
272053 
1
 
11677
2
 
8039
3
 
2650

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters294419
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 272053
92.4%
1 11677
 
4.0%
2 8039
 
2.7%
3 2650
 
0.9%

Length

2024-10-21T22:04:03.016248image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-21T22:04:03.106907image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 272053
92.4%
1 11677
 
4.0%
2 8039
 
2.7%
3 2650
 
0.9%

Most occurring characters

ValueCountFrequency (%)
0 272053
92.4%
1 11677
 
4.0%
2 8039
 
2.7%
3 2650
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 294419
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 272053
92.4%
1 11677
 
4.0%
2 8039
 
2.7%
3 2650
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 294419
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 272053
92.4%
1 11677
 
4.0%
2 8039
 
2.7%
3 2650
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 294419
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 272053
92.4%
1 11677
 
4.0%
2 8039
 
2.7%
3 2650
 
0.9%

Interactions

2024-10-21T22:03:56.607415image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:39.673899image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:41.023116image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:42.553639image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:43.887902image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:45.346555image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:46.696484image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:48.042505image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:49.542234image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:50.874397image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:52.259884image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:53.804017image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:55.214721image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:56.709667image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:39.781269image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:41.137359image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:42.657269image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:43.990815image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:45.452497image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:46.801631image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:48.145861image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:49.645893image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:50.981580image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:52.364061image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:53.913903image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:55.323720image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:56.813561image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:39.886663image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:41.243350image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:42.761660image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:44.094678image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:45.560045image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:46.906955image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:48.251770image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:49.751722image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:51.090312image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:52.469160image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:54.024867image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:55.432659image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:56.909252image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:39.984669image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:41.342602image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:42.856661image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:44.190060image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:45.658092image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:47.004564image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:48.347709image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:49.846992image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:51.191359image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:52.567008image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:54.127133image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:55.535251image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:57.007224image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:40.084753image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:41.443753image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:42.956446image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:44.287464image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:45.758059image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:47.104082image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:48.448030image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:49.947070image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:51.295062image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:52.668150image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:54.232949image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:55.640111image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:57.108576image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:40.189374image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:41.549592image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:43.060313image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:44.389223image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:45.861937image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:47.207797image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:48.550066image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:50.050678image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:51.402673image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:52.771872image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:54.341997image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:55.748159image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:57.210493image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:40.293067image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:41.655331image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:43.163924image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:44.490333image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:45.965924image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:47.310941image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:48.652792image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:50.154045image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:51.508922image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:52.875627image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:54.452266image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:55.855995image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:57.307655image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:40.393932image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:41.758326image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:43.262242image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:44.588404image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:46.066689image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:47.413306image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:48.750918image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:50.253705image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:51.611948image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:52.976138image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:54.557724image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:55.960596image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:57.407552image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:40.495498image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:41.859952image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:43.361104image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:44.687452image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:46.167670image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:47.513661image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:49.020079image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:50.352067image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:51.714394image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:53.075925image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:54.662320image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:56.065051image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:57.512723image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:40.602978image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:41.970078image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:43.475549image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:44.794041image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:46.276647image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:47.622211image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:49.127351image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:50.458879image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:51.825418image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:53.182775image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:54.775457image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:56.177922image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:57.611332image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:40.704246image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:42.074488image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:43.577532image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:44.892177image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:46.378081image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:47.723020image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:49.227026image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:50.557669image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:51.932945image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:53.282750image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:54.879108image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:56.281805image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:57.721801image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:40.814794image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:42.342260image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:43.686630image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:45.001907image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:46.490400image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:47.835489image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:49.338062image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:50.670066image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:52.049460image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:53.394372image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:54.996323image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:56.397186image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:57.826139image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:40.922809image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:42.452688image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:43.793259image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:45.107561image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:46.598616image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:47.942876image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:49.444074image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:50.777158image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:52.158750image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:53.706174image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:55.108052image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:03:56.507132image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-10-21T22:04:03.189785image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
adv_radv_sdata_rdata_sdata_sent_to_bsdist_ch_to_bsdist_to_chexpaned_energyis_targetjoin_rjoin_sranksch_rsch_ssend_codetimewho_ch
adv_r1.0000.246-0.1110.132-0.036-0.032-0.2020.2290.266-0.0010.294-0.1660.4550.0920.2430.0230.051
adv_s0.2461.0000.400-0.4890.4910.483-0.4950.0110.4830.6260.216-0.5120.1390.053-0.5010.2700.305
data_r-0.1110.4001.000-0.4150.6940.679-0.4320.3470.1670.6650.683-0.3080.5840.708-0.4590.1340.159
data_s0.132-0.489-0.4151.000-0.377-0.3790.0590.3800.232-0.3080.5710.0570.6590.2950.511-0.095-0.131
data_sent_to_bs-0.0360.4910.694-0.3771.0000.995-0.4570.3670.1450.4200.348-0.3450.2970.696-0.4520.2170.266
dist_ch_to_bs-0.0320.4830.679-0.3790.9951.000-0.4570.3540.2750.3850.747-0.3420.6220.389-0.4540.2260.275
dist_to_ch-0.202-0.495-0.4320.059-0.457-0.4571.000-0.3240.166-0.3120.4070.4310.3460.2100.265-0.245-0.275
expaned_energy0.2290.0110.3470.3800.3670.354-0.3241.0000.0240.1850.008-0.2340.0080.0150.0640.0230.057
is_target0.2660.4830.1670.2320.1450.2750.1660.0241.0000.1550.7050.1290.5880.4180.2170.4710.416
join_r-0.0010.6260.665-0.3080.4200.385-0.3120.1850.1551.0000.323-0.3230.2800.626-0.3160.0420.041
join_s0.2940.2160.6830.5710.3480.7470.4070.0080.7050.3231.0000.2690.8440.5160.5330.5590.630
rank-0.166-0.512-0.3080.057-0.345-0.3420.431-0.2340.129-0.3230.2691.0000.2050.1640.230-0.301-0.328
sch_r0.4550.1390.5840.6590.2970.6220.3460.0080.5880.2800.8440.2051.0000.4470.4400.4710.539
sch_s0.0920.0530.7080.2950.6960.3890.2100.0150.4180.6260.5160.1640.4471.0000.2750.1450.078
send_code0.243-0.501-0.4590.511-0.452-0.4540.2650.0640.217-0.3160.5330.2300.4400.2751.000-0.190-0.216
time0.0230.2700.134-0.0950.2170.226-0.2450.0230.4710.0420.559-0.3010.4710.145-0.1901.0000.983
who_ch0.0510.3050.159-0.1310.2660.275-0.2750.0570.4160.0410.630-0.3280.5390.078-0.2160.9831.000

Missing values

2024-10-21T22:03:57.960771image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-21T22:03:58.352546image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

timewho_chdist_to_chadv_sadv_rjoin_sjoin_rsch_ssch_rrankdata_sdata_rdata_sent_to_bsdist_ch_to_bssend_codeexpaned_energyis_target
0501010000.00100251000120048130.0902.470
15010105811.45061001249000.0030.080
2501010895.970710018111000.0020.180
35010102017.17071001657000.0030.090
45010102015.31071001857000.0030.090
55010108026.310710011272000.0060.120
65010102427.360710011746000.0010.070
75010103317.940710017103000.0040.170
85010102041.860710011157000.0030.100
95010108011.96071001372000.0060.120
timewho_chdist_to_chadv_sadv_rjoin_sjoin_rsch_ssch_rrankdata_sdata_rdata_sent_to_bsdist_ch_to_bssend_codeexpaned_energyis_target
29440932039010650.00126000000000.0000.002
29441032039011000.00126000000000.0000.002
29441132039010750.00126000000000.0000.002
29441232039011000.00126000000000.0000.002
29441332039011000.00126000000000.0000.002
29441432039011000.00126000000000.0000.002
29441532039051000.00126000000000.0000.002
29441632039051000.00126000000000.0000.002
29441732035060430.0015000000000.0000.012
29441832035081000.0012601100024100.0000.012

Duplicate rows

Most frequently occurring

timewho_chdist_to_chadv_sadv_rjoin_sjoin_rsch_ssch_rrankdata_sdata_rdata_sent_to_bsdist_ch_to_bssend_codeexpaned_energyis_target# duplicates
658525037051000.00126000000000.0000.00218
603316534041000.00126000000000.0000.00216
625820535061000.00126000000000.0000.00216
701429538061000.00125000000000.0000.00216
723031539041000.00126000000000.0000.00216
616619035031000.00126000000000.0000.00215
662725537061000.00126000000000.0000.00215
725732039051000.00126000000000.0000.00215
596915534021000.00126000000000.0000.00213
606517035011000.00126000000000.0000.00213